Training Video Foundation Models with NVIDIA NeMo

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Summary

This paper presents a scalable open-source pipeline using NVIDIA NeMo for training and inference of Video Foundation Models, addressing challenges in generating high-quality videos with accelerated dataset curation and parallelized training.

Video Foundation Models (VFMs) have recently been used to simulate the real world to train physical AI systems and develop creative visual experiences. However, there are significant challenges in training large-scale, high quality VFMs that can generate high-quality videos. We present a scalable, open-source VFM training pipeline with NVIDIA NeMo, providing accelerated video dataset curation, multimodal data loading, and parallelized video diffusion model training and inference. We also provide a comprehensive performance analysis highlighting best practices for efficient VFM training and inference.
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Paper page - Training Video Foundation Models with NVIDIA NeMo

Source: https://huggingface.co/papers/2503.12964 Published on Mar 17, 2025

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Abstract

A scalable open-source pipeline using NVIDIA NeMo for training and inference of Video Foundation Models addresses challenges in generating high-quality videos.

Video Foundation Models(VFMs) have recently been used to simulate the real world to train physical AI systems and develop creative visual experiences. However, there are significant challenges in training large-scale, high qualityVFMs that can generate high-quality videos. We present a scalable, open-sourceVFMtraining pipeline withNVIDIA NeMo, providing accelerated video dataset curation,multimodal data loading, and parallelizedvideo diffusion modeltraining and inference. We also provide a comprehensiveperformance analysishighlighting best practices for efficientVFMtraining and inference.

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